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A multi-seed dynamic local graph matching model for tracking of densely packed cells across unregistered microscopy image sequences

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Abstract

The tracking of plant cells in large-scale microscopy image sequences is very challenging, because plant cells are densely packed in a specific honeycomb structure, and the microscopy images can be randomly translated, rotated and scaled in the imaging process. This paper proposes a multi-seed dynamic local graph matching method to track the plant cells across unregistered microscopy image sequences, by exploiting the geometric structure and topology of cells’ relative positions as contextual information. The proposed dynamic cell matching scheme always selects the most similar cell pair in the dynamically growing neighbor set of matched cells, so it tends to prevent the matching error accumulation during the cell correspondence growing process. Furthermore, the multi-seed-based majority voting scheme can automatically rectify the matching errors produced by one seed only. Last, the cells’ lineage tracklets are associated by using the cells’ spatial–temporal context to obtain long-term trajectories. Compared with the existing local graph matching method, the experimental results show that the proposed method improves the tracking accuracy rate by about 30% in the unregistered image sequences.

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Acknowledgements

The authors would like to thank Prof. Amit Roy- Chowdhury from the Department of Electrical Engineering at the University of California, Riverside, for offering insightful suggestions on building the proposed plant cell tracking framework. We gratefully acknowledge Prof. Venugopala Reddy from the Department of Botany and Plant Sciences at the University of California, Riverside, for providing us the datasets on which results are shown. This work was supported by the National Natural Science Foundation of China (Grant Nos. 61771189 61301254 and 61471167) and Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing (No.2018007).

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Min Liu, Jieqin Li & Weili Qian

  2. Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Changsha, 410082, China

    Min Liu, Jieqin Li & Weili Qian

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  1. Min Liu
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  2. Jieqin Li
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Correspondence to Min Liu.

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Liu, M., Li, J. & Qian, W. A multi-seed dynamic local graph matching model for tracking of densely packed cells across unregistered microscopy image sequences. Machine Vision and Applications 29, 1237–1247 (2018). https://doi.org/10.1007/s00138-018-0937-8

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  • DOI: https://doi.org/10.1007/s00138-018-0937-8

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